The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Modern vehicles may be equipped with various sensing devices and systems that assist a vehicle operator in managing vehicle operation. One type of sensing system is intended to identify relative locations and trajectories of other vehicles and other objects on a highway. Exemplary systems employing sensors, such as radar and camera systems, which identify relative locations and trajectories of other vehicles and other objects on the highway include collision-avoidance systems and adaptive cruise control systems.
Sensor systems installed on vehicles are typically calibrated during the vehicle assembly process. However, sensor orientation and signal output may drift during the life of the sensor, such that the orientation of the sensor relative to the vehicle is changed. When the sensor orientation drifts, measurements become skewed relative to the vehicle. When there are multiple sensors, this concern is further complicated.
In order for the data from various sensors to be combined to produce a consistent object map, i.e. locus and trajectory of a remote object, the sensor data need to be correctly registered. That is, the relative locations of the sensors, and the relationship between their coordinate systems and the vehicle coordinate system, typically oriented to the vehicle frame, need to be determined. When a system fails to correctly account for registration errors, a result may include a mismatch between a compiled object map (sensor data) and ground truth. Examples include an overstated confidence in location and movement of a remote object (or target) such as a vehicle, and unnecessary multiplicity of tracks in an on-board tracking database, including multiple tracks corresponding to a single remote object.
Therefore, there is a need to align each individual sensor with an accuracy comparable to its intrinsic resolution, e.g., having an alignment accuracy of 0.1 degree for a sensor having an azimuth accuracy on an order of 0.1 degree. Precision sensor mounting is vulnerable to drift during the vehicle's life and difficult to maintain manually.
There is a need to ensure that signals output from sensors are aligned and oriented with a fixed coordinate system to eliminate risk of errors associated with skewed readings. Therefore, it is desirable to have a sensor system that automatically aligns sensor output to the vehicle reference coordinate system. It is also desirable to align the sensors using a tracked object as a reference and lane information on which target vehicles and the host vehicle travel, in order to facilitate regular, ongoing alignments, to improve sensor accuracy and reduce errors associated with drift.
Known approaches for object tracking and sensor registration have determined each separately. These approaches have been computationally intensive and therefore ineffective for larger numbers of targets. Sensor bias errors have been treated as parameters derived by minimizing the discrepancy between measurements and associated fused target estimated from a separated tracking module. This approach is ineffective in cases where the position of a tracked object is different from that of ground truth. Consequently, adjusting alignment parameters to minimize the discrepancy from the position of the tracked object is not sufficient to correct the misalignments of the radar and camera.
Another limitation with separate object tracking and sensor registration includes assuming the correlation between track and registration parameters are zero. This assumption is rarely true. For example, alignment parameters usually are more sensitive to far away targets. Therefore, attaching lesser weight to far away targets with high uncertainties and greater weight in target estimations with low uncertainties would result in more accurate tracking and registration.